Protein markers for identifying remodeling status of idiopathic dilated cardiomyopathy and use thereof

Abstract

The application relates to protein markers for identifying the remodeling state of idiopathic dilated cardiomyopathy and application thereof. Through systematic analysis of plasma samples of idiopathic dilated cardiomyopathy patients, it is found that the expression levels of five proteins, MYL3, ANP, ALDH6A1, PPIA and PKIA, are significantly related to the degree of cardiac remodeling. The five proteins have good diagnostic performance in disease identification, and can be used to construct a joint evaluation model, thereby providing an economical, non-invasive and high-accuracy detection and evaluation tool for judging the cardiac remodeling state of idiopathic dilated cardiomyopathy patients.

Description

Technical Field

[0001] This application belongs to the field of medical diagnostic reagent technology, specifically relating to protein biomarkers for identifying the remodeling state in idiopathic dilated cardiomyopathy and their applications. Background Technology

[0002] Idiopathic dilated cardiomyopathy (iDCM) is a common and rapidly progressing non-ischemic cardiomyopathy with significant heterogeneity in disease phenotype and clinical prognosis. Cardiac remodeling is the core pathological process in the occurrence and progression of iDCM, manifesting not only as left ventricular dilation and decreased systolic function, but also often accompanied by multidimensional changes such as right ventricular dysfunction, atrial dysfunction, and myocardial fibrosis. These remodeling changes are closely associated with adverse endpoint events such as recurrent hospitalizations for heart failure, cardiac death, implantation of cardiac assist devices, and heart transplantation, and are important factors determining patient prognosis.

[0003] Currently, the assessment of iDCM cardiac remodeling primarily relies on cardiac imaging examinations. Cardiac magnetic resonance imaging (MRI) has high accuracy in assessing cardiac chamber structure, functional quantification, and histological characterization, and is considered the gold standard for evaluating cardiac remodeling. However, in practical clinical applications, cardiac MRI is limited by factors such as high examination costs, strong reliance on equipment and professional personnel, making widespread adoption and dynamic follow-up difficult. Conventional echocardiography, on the other hand, relies heavily on overall functional indicators and has limited ability to identify complex, multidimensional cardiac remodeling phenotypes, leading to difficulties in timely identification of some high-risk patients. Therefore, existing imaging assessment methods still have certain limitations in the refined classification and early identification of iDCM cardiac remodeling status, failing to fully meet the clinical needs for precise assessment and individualized management.

[0004] In recent years, plasma proteomics, as an emerging systems biology detection method, has been increasingly applied to cardiovascular disease research. Blood samples are widely available and easy to collect, and can reflect, to some extent, pathophysiological changes closely related to cardiac remodeling, such as inflammatory responses, metabolic disorders, and extracellular matrix remodeling, thus possessing potential value for assisting in disease assessment and monitoring. However, current research on the application of plasma proteomics in assessing the state of iDCM cardiac remodeling is still relatively limited. Existing studies mostly focus on exploratory analyses of single or a few proteins, and a stable and reliable biomarker system and corresponding analytical methods that can reflect the state of iDCM cardiac remodeling have not yet been established. The clinical applicability and systematic validation of related technical solutions still need further improvement. Summary of the Invention

[0005] Based on this, one embodiment of this application provides a blood protein biomarker for identifying the cardiac remodeling state in patients with idiopathic dilated cardiomyopathy, and proposes its application in the preparation of diagnostic or assessment products for the cardiac remodeling state in patients with idiopathic dilated cardiomyopathy.

[0006] The technical solutions include the following:

[0007] In a first aspect, this application provides protein biomarkers for identifying the state of cardiac remodeling in patients with idiopathic dilated cardiomyopathy, said protein biomarkers including one or more of the following: Aldehyde Dehydrogenase 6 Family Member A1 (ALDH6A1), Peptidylprolyl Isomerase A (PPIA), Protein Kinase Inhibitor Alpha (PKIA), Myosin Light Chain 3 (MYL3), and A-type Natriuretic Peptide (ANP).

[0008] In one embodiment, compared with patients with milder cardiac remodeling (or degree of cardiac remodeling) of idiopathic dilated cardiomyopathy, plasma samples from patients with more severe cardiac remodeling (or degree of cardiac remodeling) showed significantly reduced expression levels of ALDH6A1, PPIA, and PKIA, and significantly increased expression levels of MYL3 and ANP.

[0009] In one embodiment, the sample includes plasma, serum, or whole blood.

[0010] Secondly, this application provides the application of the detection reagent for the expression level of the above-mentioned protein markers in the preparation of diagnostic or assessment products for evaluating the status, degree, phenotype, or subtype of cardiac remodeling in patients with idiopathic dilated cardiomyopathy.

[0011] In one embodiment, the product is used to determine whether the cardiac remodeling status (or degree) of a patient with idiopathic dilated cardiomyopathy is mild or severe.

[0012] It should be noted that mild cardiac remodeling is categorized as mild or less severe, while severe cases are categorized as severe or more severe. Grading the patient's cardiac remodeling status helps to differentiate the severity of the condition, thus providing a reference for subsequent treatment.

[0013] In one embodiment, the detection reagent is selected from one or more of the following methods: liquid chromatography, mass spectrometry, and immunoassay.

[0014] In one embodiment, the immunoassay includes one or more of Western blot analysis, radioimmunoassay, immunofluorescence assay, immunoprecipitation, immunodiffusion, electrochemiluminescence immunoassay, ELISA assay, and immunopolymerase chain reaction.

[0015] In one embodiment, the detection reagent is a reagent suitable for liquid chromatography-tandem mass spectrometry (LC-MS / MS) or double antibody sandwich ELISA.

[0016] In one embodiment, mass spectrometry based on protein characteristic tag peptides is employed.

[0017] In one embodiment, the detection reagent comprises an antibody or a functional fragment thereof.

[0018] In one embodiment, the product includes a reagent kit, a chip, a test strip, a system, a device, or an apparatus.

[0019] In one embodiment, the kit is a mass spectrometry identification tag peptide kit or an enzyme-linked immunosorbent assay (ELISA) kit. In one embodiment, mass spectrometry identification tag peptide refers to its use in a data-independent mass spectrometry acquisition mode.

[0020] Thirdly, this application provides products for identifying or assessing the cardiac remodeling status of patients with idiopathic dilated cardiomyopathy, the products including a detection reagent for the expression level of the protein biomarker.

[0021] Fourthly, this application provides the application of protein biomarkers in constructing a diagnostic model to assess the cardiac remodeling status of patients with idiopathic dilated cardiomyopathy, wherein the protein biomarkers include MYL3, ANP, ALDH6A1, PPIA, and PKIA.

[0022] Fifthly, this application provides a method for constructing a diagnostic model to assess the cardiac remodeling status of patients with idiopathic dilated cardiomyopathy, the method comprising constructing the model based on the expression information of protein markers MYL3, ANP, ALDH6A1, PPIA and PKIA.

[0023] In one embodiment, the construction method includes: constructing the diagnostic model using a machine learning algorithm based on the expression level data of the protein biomarkers in patients with idiopathic dilated cardiomyopathy. Optionally, the machine learning algorithm includes linear regression, support vector machine, nearest neighbor / k-nearest neighbor, logistic regression, decision tree, k-means, random forest, Naive Bayes, dimensionality reduction, and gradient boosting. Optionally, the machine learning algorithm is logistic regression.

[0024] In one embodiment, the diagnostic model includes MYL3, ANP, ALDH6A1, PPIA, and PKIA protein markers, and the diagnostic model includes the following calculation formula:

[0025] logit(p) = 4.0877 + 0.6942 ×ANP + 0.2500 × MYL3 - 1.3552 × PPIA -0.6223 × PKIA - 0.8257 × ALDH6A1

[0026] Wherein, logit(p) represents the probability value, ANP represents the expression level of ANP protein, MYL3 represents the expression level of MYL3 protein, ALDH6A1 represents the expression level of ALDH6A1 protein, PPIA represents the expression level of PPIA protein, and PKIA represents the expression level of PKIA protein.

[0027] In one embodiment, when p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are considered to be in a state of severe cardiac remodeling; when p < 0.5, patients with idiopathic dilated cardiomyopathy are considered to be in a state of mild cardiac remodeling.

[0028] Sixthly, this application provides a diagnostic model constructed using the construction method described in the fifth aspect.

[0029] In a seventh aspect, this application provides a diagnostic system or device for assessing the cardiac remodeling status in patients with idiopathic dilated cardiomyopathy, the diagnostic system comprising the following modules (1) to (3):

[0030] (1) Data acquisition module, used to acquire the expression levels of protein markers in the sample of the subject to be tested, the protein markers including MYL3, ANP, ALDH6A1, PPIA and PKIA.

[0031] (2) Analysis module, used to analyze the expression levels of protein markers obtained by the data acquisition module to determine whether the subject’s cardiac remodeling state is mild or severe.

[0032] In one embodiment, the analysis module is used to construct a diagnostic model based on the protein biomarker expression levels obtained by the data acquisition module, thereby enabling the identification of cardiac remodeling status in patients with idiopathic dilated cardiomyopathy.

[0033] In one embodiment, the analysis module is used to provide the protein biomarker expression levels obtained by the data acquisition module as input data to the constructed diagnostic model, and to analyze whether the subject belongs to a mild or severe state of cardiac remodeling.

[0034] In one embodiment, the diagnostic model is constructed based on the expression levels of the aforementioned protein biomarkers in patients with idiopathic dilated cardiomyopathy, and is modeled using logistic regression or other machine learning algorithms.

[0035] It should be noted that, in addition to logistic regression, other machine learning algorithms can also be used to model the expression levels of the protein biomarkers, including but not limited to linear regression, support vector machine, nearest neighbor or k-nearest neighbor, decision tree, k-means, random forest, Naive Bayes, dimensionality reduction, and gradient enhancement algorithms. Any system that constructs a diagnostic model based on the expression levels or content of the protein biomarkers described in this application and is used for identifying the cardiac remodeling state in patients with idiopathic dilated cardiomyopathy falls within the scope of protection of this application.

[0036] In one embodiment, the diagnostic model is the diagnostic model obtained by the construction method described in the fifth aspect.

[0037] (3) Result output module, used to output the analysis results of the analysis module to obtain the diagnostic results of the subject's cardiac remodeling status.

[0038] Eighthly, this application provides a computer-readable storage medium including a computer program executed by a processor, wherein the computer program, when executed by the processor, implements the functions of different modules in the system.

[0039] Ninthly, this application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the functions of the diagnostic system.

[0040] It should be understood that the terms "system," "apparatus," and / or "device" as used herein are used to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions. Those skilled in the art will recognize that this application can be implemented as a method or a computer program product. Therefore, the disclosure of this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some specific embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable media, which contains computer-readable program code.

[0041] Based on a quantitative proteomics method using mass spectrometry, this application identified five proteins in the plasma of patients with idiopathic dilated cardiomyopathy that are significantly associated with cardiac remodeling. These proteins were then used to construct a diagnostic model, providing an economical, non-invasive, and accurate diagnostic tool for cardiac remodeling.

[0042] Tenthly, this application provides a method for identifying the cardiac remodeling state in patients with idiopathic dilated cardiomyopathy, comprising:

[0043] (1) Collect plasma samples from the subjects;

[0044] (2) Detect the protein expression levels of MYL3, ANP, ALDH6A1, PPIA and PKIA in plasma samples;

[0045] (3) Based on the protein expression level, a pre-established diagnostic model is used for analysis to determine whether the subject belongs to a mild or severe state of cardiac remodeling.

[0046] The above method is a non-invasive diagnostic method for cardiac remodeling status in patients with idiopathic dilated cardiomyopathy, developed based on plasma proteomics and machine learning.

[0047] Compared with traditional technologies, this application has the following advantages:

[0048] This application employs a mass spectrometry method based on characteristic protein-tagged peptides to detect the expression levels of MYL3, ANP, ALDH6A1, PPIA, and PKIA in plasma. The expression levels of these five biomarkers demonstrate strong discriminative ability regarding the cardiac remodeling state in idiopathic dilated cardiomyopathy, thus providing a novel non-invasive diagnostic method for identifying this disease state.

[0049] This application provides a non-invasive, stable plasma testing method suitable for clinical translation, offering a new approach for the accurate identification and individualized management of cardiac remodeling in patients with idiopathic dilated cardiomyopathy. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application and to more completely understand this application and its beneficial effects, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a correlation heatmap; the figure shows the Spearman correlation coefficient matrix between 27 cardiovascular magnetic resonance (CMR) parameters used for principal component analysis, with color intensity indicating the magnitude of the correlation coefficient.

[0052] Figure 2 The graph is a scree plot for principal component analysis; the bar chart represents the proportion of variance explained by each principal component, and the line chart represents the proportion of variance explained cumulatively.

[0053] Figure 3 This is a heatmap of the loading values ​​of the CMR parameters after principal component analysis. The figure shows the distribution of the loading values ​​of the first four principal components (PC1–PC4) obtained after principal component analysis of 27 CMR parameters and orthogonal rotation by Varimax. The colors indicate the magnitude and direction of the loading values ​​of each parameter on the corresponding principal component.

[0054] Figure 4 Figure A shows the clustering results of patients based on risk scores; Figure B shows that the within-group sum of squares elbow method indicates that K=2 is the optimal number of clusters; Figure C shows that the silhouette coefficient analysis shows that the two types of patients have high consistency; Figure C shows that the kernel density distribution plot shows that the risk score distributions of the two types of patients are clearly distinguishable and have little overlap.

[0055] Figure 5 Figure A shows the distribution of the two types of patients on each principal component score, defined as the advanced remodeling subtype and the compensated function subtype, respectively. Figure B shows the Kaplan-Meier event-free survival curves for the two types of patients, with the horizontal axis representing follow-up time and the vertical axis representing survival probability.

[0056] Figure 6 Figure 1 is a schematic diagram of the protein screening process. Figure A is a LASSO regression coefficient path diagram, showing the coefficient shrinkage process of 1,309 differentially expressed proteins under different log-transformation penalty parameters λ. The dashed line represents the optimal penalty parameter (λ-min) determined through cross-validation. Figure B is a 10-fold cross-validation curve, showing the changes in the mean squared error (MSE) of the model under different log(λ) values, where λ-min corresponds to the minimum cross-validation error. Figure C shows 20 candidate proteins screened after LASSO regression and correction for age, sex, and NT-proBNP. Their regression coefficients are displayed in order of absolute value. In the bar chart, blue and red represent proteins that are positively and negatively correlated with the outcome, respectively.

[0057] Figure 7The figure shows the expression distribution of candidate proteins in different subtypes. The figure is presented in violin form to show the expression levels of ALDH6A1, MYL3, PPIA, PKIA and ANP in the two types of patients. Among them, Cluster 1 represents the severe cardiac remodeling and Cluster 2 represents the functional compensation type.

[0058] Figure 8 The figure shows the receiver operating characteristic (ROC) curves. It compares the curve distributions of the five-protein combined model and NT-proBNP in identifying cardiac remodeling states, and labels the area under the curves and confidence intervals.

[0059] Figure 9 Kaplan–Meier survival curves for the external validation cohort; the vertical axis represents the survival probability, the horizontal axis represents the follow-up time (months), and the number of people at risk at the corresponding time point is marked below the curve. The survival difference between the two cardiac remodeling phenotypes was assessed by the log-rank test.

[0060] Figure 10 The expression distribution of five plasma proteins in two patient groups is shown in a violin diagram. The expression levels of ALDH6A1, MYL3, PPIA, PKIA and ANP are shown in the form of a violin diagram. Cluster 1 represents the more severe cardiac remodeling and cluster 2 represents the functional compensatory type.

[0061] Figure 11 ROC curves for different cardiac remodeling phenotypes in the five-protein combined model; the area under the curve for the model shown in the figure is 0.808. Detailed Implementation

[0062] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, a detailed description of specific embodiments of this application is provided below. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0064] In this document, the term "and / or" includes any and all combinations of one or more of the related listed items.

[0065] In this study, severe remodeling or a more severe state of cardiac remodeling refers to a subtype of idiopathic dilated cardiomyopathy patients identified with higher risk scores after dimensionality reduction analysis to construct a comprehensive risk score characterizing the state of cardiac remodeling based on cardiac structural and functional parameters obtained from cardiac magnetic resonance imaging (CMR) and subsequent classification using unsupervised clustering. This subtype exhibits characteristic patterns in the principal component score spectrum associated with aggravated cardiac structural remodeling and functional impairment, and demonstrates poorer clinical outcomes during follow-up. These patients present with significantly impaired overall systolic and dystrophic function, right atrial mechanical dysfunction, and a more severe degree of myocardial remodeling, resulting in a higher risk of adverse clinical outcomes.

[0066] In this study, "relatively compensated" or "mild cardiac remodeling" refers to a subtype of idiopathic dilated cardiomyopathy patients identified by dimensionality reduction analysis of cardiac structural and functional parameters obtained from cardiac magnetic resonance imaging (CMR) to construct a risk score comprehensively representing the state of cardiac remodeling, followed by unsupervised clustering. This subtype exhibits a characteristic pattern in the principal component score spectrum associated with relatively preserved cardiac function and mild remodeling, and demonstrates relatively stable clinical outcomes during follow-up. These patients show relatively preserved overall cardiac function and atrial biomechanics, mild myocardial remodeling, and a relatively good clinical prognosis.

[0067] In this article, the term "NT-proBNP" refers to N-terminal pro–B-type natriuretic peptide, a biomarker secreted by ventricular myocytes when cardiac pressure load increases. It reflects cardiac function and is widely used to assess the severity and prognostic risk of heart failure.

[0068] This application relates to five biomarkers derived from plasma samples: myosin light chain 3 (MYL3, UniProt ID: P08590), anaerobic peptide A (ANP, UniProt ID: P01160), aldehyde dehydrogenase 6 family member A1 (ALDH6A1, UniProt ID: Q02252), peptidylprolyl isomerase A (PPIA, UniProt ID: P62937), and protein kinase inhibitor alpha (PKIA, UniProt ID: P61925). Among them, MYL3 is an important structural protein of the myocardial contractile apparatus, and changes in its expression level can reflect the state of myocardial structure and contractile function; ANP is a cardiac-derived endocrine peptide hormone that is closely related to ventricular load and cardiac remodeling; ALDH6A1 participates in mitochondrial-related metabolic processes and plays a role in energy metabolism homeostasis and oxidative stress regulation; PPIA, as a molecular chaperone protein, participates in protein folding and cellular stress response; and PKIA participates in intracellular signal regulation by inhibiting the protein kinase A signaling pathway.

[0069] Currently, there are no publicly available documents or patent reports that utilize the expression levels of MYL3, ANP, ALDH6A1, PPIA, and PKIA in plasma, or models constructed using these in combination, for non-invasive diagnostic assessment of cardiac remodeling status in patients with idiopathic dilated cardiomyopathy.

[0070] This application used data-independent acquisition (DIA) mass spectrometry analysis and multivariate statistical modeling to screen and establish five plasma proteins—MYL3, ANP, ALDH6A1, PPIA, and PKIA—as core biomarkers. The logistic regression model constructed based on these five proteins demonstrated excellent diagnostic efficacy (AUC = 0.814), significantly superior to NT-proBNP.

[0071] The embodiments of this application will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. For experimental methods in the following embodiments where specific conditions are not specified, please refer to the guidelines given in this application, or follow experimental manuals or conventional conditions in the art, or follow the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0072] In the specific embodiments described below, the measurement parameters involving raw material components may have slight deviations within the weighing accuracy range unless otherwise specified. Temperature and time parameters are subject to acceptable deviations due to instrument testing accuracy or operational precision.

[0073] Example 1

[0074] 1. Materials and Reagents

[0075] The main instruments used in these examples included an Orbitrap Astral high-resolution mass spectrometer (Thermo Fisher Scientific) and a Vanquish Neo ultra-high performance liquid chromatography system (Thermo Fisher Scientific). The magnetic bead-type plasma protein processing kit used in sample pretreatment was purchased from Shanghai Bopu Biotechnology Co., Ltd. Trypsin and Lys-C / trypsin complex enzymes were purchased from Promega. Ammonium bicarbonate, tris(2-carboxyethyl)phosphine (TCEP), chloroacetamide (CAA), and other reagents were purchased from Sigma-Aldrich. Acetonitrile, formic acid, and HPLC-grade water were purchased from JT Baker. All chemical reagents were of analytical grade or mass spectrometry grade.

[0076] 2. Sample Source

[0077] This study included 175 plasma samples, comprising 125 patients with idiopathic dilated cardiomyopathy (DCM) and 50 healthy controls. All plasma samples from DCM patients were collected at the time of initial diagnosis, while samples from healthy controls were collected from individuals undergoing routine physical examinations during the same period. After collection, whole blood samples were centrifuged to separate plasma, aliquoted, and immediately stored at -80 °C to avoid repeated freeze-thaw cycles for subsequent proteomics analysis.

[0078] 3. Plasma sample processing and proteolytic digestion

[0079] Take 50 µL of plasma sample from each case and use the magnetic bead plasma protein processing kit according to the manufacturer's instructions. First, add 20 µL of magnetic beads to the wash buffer and wash three times, mixing at 1300 rpm for 30 seconds each time. After magnetic adsorption for 1 minute, discard the supernatant. Then, add 50 µL of plasma sample and 50 µL of incubation buffer to the magnetic beads, mix at 1300 rpm at room temperature for 10 minutes to allow plasma proteins to fully bind to the surface of the magnetic beads. After incubation, discard the supernatant by magnetic adsorption and wash four times consecutively with wash buffer to remove non-specific components.

[0080] In the elution step, 40 µL of elution buffer was added to the magnetic beads and mixed at room temperature for 20 minutes to obtain the eluted protein. Then, 1 µL of 400 mM TCEP and 2 µL of 800 mM CAA were added to the eluent, and the reaction was carried out at 95 °C for 3 minutes to complete the protein reduction and alkylation. After cooling, 160 µL of 50 mM ammonium bicarbonate buffer was added, along with 0.5 µg of Lys-C / trypsin complex enzyme, and the reaction was incubated overnight at 37 °C. The reaction was terminated by adding a stop solution. The digested peptide solution was transferred to a C18 StageTip for desalting, and washed sequentially with methanol and 0.1% trifluoroacetic acid solution, followed by elution with 50% acetonitrile solution containing 0.1% trifluoroacetic acid. The eluent was dried by vacuum centrifugation and then reconstituted in a buffer containing 2% acetonitrile and 0.1% formic acid for subsequent liquid chromatography-mass spectrometry analysis.

[0081] 4. LC-MS / MS Analysis of DIA Data Acquisition

[0082] Peptide samples were separated using a Vanquish Neo ultra-high performance liquid chromatography system and detected by an Orbitrap Astral mass spectrometer. Samples were loaded onto a 50 cm µPAC™ Neo column, with 0.1% formic acid aqueous solution as mobile phase A and 0.1% formic acid in 80% acetonitrile as mobile phase B, and separation was achieved under gradient elution conditions for 8 minutes. Mass spectrometry data were acquired in data-independent acquisition (DIA) mode, with a primary mass spectrometry scan range of m / z 380–980 and a resolution of 240,000; and a secondary mass spectrometry scan range of m / z 150–2000, an isolation window width of 2 m / z, and a cycle time of 0.6 seconds.

[0083] 5. Sequence database retrieval and quantitative analysis

[0084] The obtained raw DIA data were analyzed using DIA-NN (version 1.8.18) software, and the UniProtKB / Swiss-Prot human protein database (October 2023 version) was searched. Search criteria were set to trypsin digestion, allowing a maximum of one missed cleavage site, cysteine ​​alkylation as a fixed modification, and N-terminal acetylation and methionine oxidation as variable modifications. False positive rate (FDR) was controlled to be less than 1% for both peptide and protein levels.

[0085] 6. Bioinformatics Analysis

[0086] Protein quantification results were imported into R statistical software for differential expression analysis. Differentially expressed proteins between patients with idiopathic dilated cardiomyopathy and healthy controls were screened by setting a Fold change >1.5 or <0.68 and a p-value <0.05 after FDR correction. Based on these screening criteria, a total of 1,309 proteins with significant differential expression between the two groups were identified. Protein functional annotation and pathway enrichment analysis were performed using the UniProt, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Statistical significance was assessed using Fisher's exact test combined with multiple test corrections to reveal the holistic biological characteristics of cardiac remodeling associated with dilated cardiomyopathy.

[0087] 7. Construction of cardiac remodeling phenotypes based on cardiac magnetic resonance parameters

[0088] To construct an objective, reproducible, and clinically valuable classification standard for cardiac remodeling in patients with idiopathic dilated cardiomyopathy, this embodiment systematically models and defines the cardiac remodeling phenotype based on quantitative parameters from cardiac magnetic resonance imaging (CMR). Unlike previous methods that relied on single clinical indicators such as left ventricular ejection fraction or functional classification for classification, this embodiment directly constructs a remodeling phenotype system reflecting cardiac structure, function, and histological characteristics based on multidimensional CMR imaging parameters.

[0089] The cardiac remodeling classification criteria in this application consist of four key steps:

[0090] (1) Data input and preprocessing;

[0091] (2) Principal component analysis (PCA) and calculation of reconstructed axis scores;

[0092] (3) Construction of prognostic risk score based on reconstructed axis;

[0093] (4) K-means clustering classification based on risk scores.

[0094] All analyses were performed using the R programming language.

[0095] 7.1 Fixed set of input parameters and preprocessing

[0096] To comprehensively reflect the structural, functional, and myocardial histological characteristics of the heart, this application selects 27 quantitative CMR parameters as modeling inputs, specifically including:

[0097] (1) Left ventricular structural and functional parameters: left ventricular end-diastolic volume index (LVEDVI), left ventricular end-systolic volume index (LVESVI), left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI), and left ventricular global radial strain (LVGRS), global circumferential strain (LVGCS) and global longitudinal strain (LVGLS).

[0098] (2) Structural and functional parameters of the right ventricle: right ventricular end-diastolic volume index (RVEDVI), right ventricular end-systolic volume index (RVESVI), right ventricular ejection fraction (RVEF), right ventricular mass index (RVMI), and right ventricular global radial strain (RVGRS), global circumferential strain (RVGCS), and global longitudinal strain (RVGLS);

[0099] (3) Left atrial phase function and volume parameters: left atrial maximum volume index (LAVImax), left atrial total emptying fraction (LAEFtotal), and left atrial reserve strain (εs-LA), conduction strain (εe-LA) and boost strain (εa-LA);

[0100] (4) Right atrial phase function and volume parameters: right atrial maximum volume index (RAVImax), right atrial total emptying fraction (RAEFtotal), right atrial reserve strain (εs-RA), conduction strain (εe-RA), and boost strain (εa-RA);

[0101] (5) Myocardial histology parameters: delayed enhancement mass (LGEmass), extracellular volume fraction (ECV), and T1 relaxation time before contrast agent enhancement (preT1).

[0102] Data preprocessing methods:

[0103] Import the input data into the R language dataframe Patient_CMR_Data as a CSV or Excel file.

[0104] After performing a log2 transformation on the skewed distributed variables (LGEmass, ECV, preT1), all variables are standardized.

[0105]

[0106] in: The original variable value, This represents the mean of the variable in the discovery queue. The standard deviation is denoted as .

[0107] After standardization, the mean of the variables is 0 and the variance is 1 to ensure the repeatability and dimensional consistency of subsequent analyses.

[0108] 7.2 PCA Dimensionality Reduction and Reconstruction Axis Construction

[0109] (1) Correlation analysis:

[0110] Use `R cor()` to calculate the pairwise Pearson correlation coefficients of the 27 parameters and plot a heatmap to show the relationships between the variables. Figure 1 ).

[0111] (2) Principal component analysis

[0112] Principal component analysis was performed on the standardized variables using the R function prcomp().

[0113] The number of principal components retained was determined based on the following criteria: a screen plot was plotted to show the proportion of variance explained by each principal component and the cumulative explained variance; the number of principal components to retain was determined when the cumulative explained variance was ≥70% and the curve showed an inflection point. Ultimately, 4 principal components were retained. Figure 2 ).

[0114] (3) Rotation optimization

[0115] To improve interpretability, the principal() function in the R psych package is used with rotate="varimax" to orthogonally rotate the loading matrix, resulting in a rotated loading matrix.

[0116] (4) Reconstructing axis definition:

[0117] In the discovery queue, the loading coefficients of each variable on the four principal components are extracted based on the above rotational loading results, forming a 27 × 4 loading coefficient matrix. This matrix reflects the contribution weight of each CMR parameter on each principal component and serves as the fixed weight basis for subsequent reconstruction axis calculations.

[0118] Based on the rotating load structure, the four principal components are defined as four cardiac remodeling axes ( Figure 3 )

[0119] Reconstruction axis 1 (PC1): left and right ventricular systolic function and strain parameters, reflecting the overall contraction-deformation state.

[0120] Remodeling axis 2 (PC2): Biventricular volume and myocardial mass parameters, reflecting the degree of ventricular structural remodeling.

[0121] Reconstruction axis 3 (PC3): Right atrial volume and strain parameters, reflecting the mechanical functional state of the right atrium.

[0122] Reconstructed axis 4 (PC4): ECV and preT1 parameters, reflecting the level of diffuse myocardial fibrosis.

[0123] (5) Calculation of reconstructed axis score:

[0124] The score for each patient on the k-th reconstruction axis is calculated using the following formula:

[0125]

[0126] in Let i be the rotational load on the principal component k. These are the standardized input variables.

[0127] The PC value for each patient is automatically generated by predict(prcomp_model).

[0128] 7.3 Construction of Prognostic Risk Score Based on Reconstruction Axis

[0129] (1) Multivariate Cox model

[0130] A multivariate Cox proportional hazards model was constructed using the `coxph()` function from the `survival` package in the R language, with PC1–PC4 included as continuous predictor variables for model fitting. β1, β2, β3, and β4 are the regression coefficients (log hazard ratios) of PC1–PC4 in the Cox model, obtained through partial likelihood estimation. These regression coefficients were used to construct individualized prognostic risk scores.

[0131] (2) Calculation of individualized risk score

[0132] Based on the Cox regression coefficients β1–β4, calculate the prognostic risk score (RiskScore) for each patient:

[0133]

[0134] The RiskScore is used to quantify the overall reconstruction risk for each patient, providing input features for subsequent clustering.

[0135] 7.4 K-means Clustering Based on Risk Scores

[0136] (1) Clustering input

[0137] Unsupervised clustering is performed using RiskScore as the input feature.

[0138] Since RiskScore is a continuous variable, the K-means algorithm is used to automatically determine the fractal boundary based on minimizing the intra-cluster squared error, rather than manually setting a threshold.

[0139] (2) Determining the optimal number of clusters

[0140] The optimal number of categories is determined using the following method:

[0141] Elbow method: Comparing the trend of changes in the sum of squares within clusters

[0142] Silhouette coefficient: assesses intra-class consistency

[0143] The optimal number of clusters was finally determined to be K=2 ( Figure 4 AB).

[0144] (3) Cluster stability verification

[0145] Kernel density estimation (KDE) was used to observe intra-cluster concentration and inter-cluster separation. Figure 4 C), verify the stability of the fractal.

[0146] (4) Classification results

[0147] Ultimately, two stable cardiac remodeling phenotypes were obtained ( Figure 5 A):

[0148] Severe reconstruction (n=58): characterized by significant structural and functional impairment;

[0149] Functional relative compensation type (n=67): characterized by the relative preservation of structural functions.

[0150] R implementation: stats::kmeans() + cluster::silhouette() + ggplot2 for density distribution visualization, outputting Patient_Cluster_Label as the final classification label.

[0151] 7.5 Verification of Technical Effectiveness

[0152] Survival analysis confirmed that patients with more severe remodeling had significantly lower event-free survival than those with relatively compensated function (log-rank P = 0.0016). Figure 5 B) indicates that the cardiac remodeling classification based on CMR parameters constructed in this embodiment has clear accuracy and stratification value in predicting clinical outcomes.

[0153] The above results further demonstrate that the cardiac remodeling classification system not only has statistical stability but also verifiable predictive ability in clinical risk assessment. Therefore, it can serve as a prerequisite classification standard for subsequent screening of plasma protein biomarkers, construction of diagnostic models, and development of related testing products based on cardiac remodeling phenotypes.

[0154] 8. Construction and performance evaluation of the diagnostic model

[0155] Based on the cardiac remodeling phenotypes constructed using cardiac magnetic resonance parameters, and combined with 1,309 candidate proteins obtained from differential expression analysis, this embodiment further employs the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm for feature screening to construct a plasma protein diagnostic model for identifying different cardiac remodeling phenotypes in patients with idiopathic dilated cardiomyopathy. During the modeling process, age, sex, and NT-proBNP were included as covariates for model correction to reduce the impact of potential confounding factors on the model results. After LASSO regression analysis, 20 candidate proteins with potential diagnostic value were selected (…). Figure 6 AC). Subsequently, based on the magnitude of the LASSO regression coefficient and the known mechanisms of action of the proteins in the biological processes related to dilated cardiomyopathy and cardiac remodeling, the above candidate proteins were further screened, and ALDH6A1, MYL3, PPIA, PKIA, and ANP were selected as the core biomarkers. Figure 7 ).

[0156] The five differentially expressed proteins were jointly modeled, and a diagnostic model was constructed using logistic regression to generate a diagnostic score logit(p), which was used to distinguish different disease states and assess the degree of cardiac remodeling in patients. The logistic regression equation of the constructed joint diagnostic model is as follows:

[0157] logit(p) = 4.0877 + 0.6942 ×ANP + 0.2500 × MYL3 - 1.3552 × PPIA -0.6223 × PKIA - 0.8257 × ALDH6A1

[0158] Wherein, MYL3, ANP, ALDH6A1, PPIA, and PKIA represent the expression levels of the corresponding proteins. In this embodiment, the probability value output by the model is p. When p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are judged to be in a state of severe cardiac remodeling; when p < 0.5, patients with idiopathic dilated cardiomyopathy are judged to be in a state of mild cardiac remodeling.

[0159] Receiver operating characteristic (ROC) curves were performed on individual proteins ALDH6A1, MYL3, PPIA, PKIA, and ANP. The results showed that each protein exhibited some diagnostic value, but the overall diagnostic performance was at a moderate level, suggesting that a single protein is insufficient to fully reflect the complex cardiac remodeling characteristics of patients with dilated cardiomyopathy. In contrast, the diagnostic performance was significantly improved after combining the five proteins into a single model. The area under the curve (AUC) of the five-protein combined diagnostic model on the training set was 0.814 (95% confidence interval: 0.740–0.887), with a sensitivity of 80.3% and a specificity of 72.4%, demonstrating good discriminative ability. The five-protein combined diagnostic model was further compared with NT-proBNP, a commonly used clinical biomarker for heart failure. The results showed that the AUC of NT-proBNP in this embodiment was 0.698, while the AUC of the five-protein combined diagnostic model was significantly higher than that of NT-proBNP. The DeLong test showed that the difference was statistically significant (P = 0.038). Figure 8 ).

[0160] 9. External cohort validation of the diagnostic model

[0161] To evaluate the generalization ability and robustness of the cardiac remodeling classification model constructed based on CMR multiple remodeling axes and plasma protein biomarkers, this embodiment further validates the model in an independent external validation cohort. The external validation cohort included 91 patients with idiopathic dilated cardiomyopathy from Beijing Anzhen Hospital, whose clinical data collection, follow-up endpoints, and cardiac magnetic resonance imaging procedures were consistent with the aforementioned discovery cohort.

[0162] During external validation, the key CMR parameters with large absolute loading values ​​and high contributions to the corresponding remodeling axis were first selected based on the cardiac remodeling axis loading coefficient matrices obtained from the discovery cohort through principal component analysis (PCA). These key parameters were standardized using the mean and standard deviation of the discovery cohort to eliminate the influence of dimensional differences. No model retraining or parameter re-estimation was performed during external validation.

[0163] Subsequently, based on the loading coefficients corresponding to each key CMR parameter in the discovery cohort, the standardized parameters were weighted and summed to reconstruct the scores of each subject in the external validation cohort on each cardiac remodeling axis. The first remodeling axis primarily reflects ventricular systolic function characteristics and is calculated based on LVEF, LVGCS, LVGRS, RVGCS, and RVGRS; the second remodeling axis primarily reflects ventricular structural remodeling characteristics and is calculated based on LVEDVI, LVESVI, LVMI, RVEDVI, RVESVI, and RVMI; the third remodeling axis primarily reflects right atrial structural and functional characteristics and is calculated based on RAVI, RAEF, and εs-RA; and the fourth remodeling axis primarily reflects myocardial histological characteristics and is calculated based on native T1 and ECV.

[0164] After obtaining multiple cardiac remodeling axis scores from patients in the external validation cohort, centroid mapping was used to map these patients to pre-defined cardiac remodeling phenotype categories in the discovery cohort, thus completing the external validation of the cardiac remodeling classification. Results showed that, consistent with the discovery cohort, patients with more severe remodeling phenotypes (n = 44) in the external validation cohort had significantly lower event-free survival than those with relatively compensated functional remodeling (n = 47) (log-rank P = 0.03). Figure 9 This suggests that the cardiac remodeling classification based on CMR parameters has good stability and consistent prognostic ability in independent populations.

[0165] Furthermore, to further verify the applicability of the plasma protein diagnostic model in an external cohort, this embodiment used enzyme-linked immunosorbent assay (ELISA) to quantify five core candidate proteins: ALDH6A1, MYL3, PPIA, PKIA, and ANP. The results showed that all five proteins exhibited significant differential expression between the two cardiac remodeling phenotypes. Figure 10 Based on the model parameters and discrimination thresholds determined in the discovery cohort, the five proteins were combined and applied to an external validation cohort. The area under the receiver operating characteristic (AUC) for distinguishing different cardiac remodeling phenotypes was 0.808, corresponding to a sensitivity of 78.1% and a specificity of 70.2%. This indicates that the combined protein diagnostic model still possesses good diagnostic performance and generalization ability in independent populations. Figure 11 ).

[0166] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims, and the specification can be used to interpret the content of the claims.

Claims

1. The application of a reagent for detecting the expression level of protein biomarkers in the preparation of a product for assessing the cardiac remodeling status in patients with idiopathic dilated cardiomyopathy, wherein the protein biomarkers are ALDH6A1, PPIA, PKIA, MYL3, and ANP.

2. The application according to claim 1, characterized in that, The product is used to determine whether a patient's cardiac remodeling is mild or severe. The lighter or heavier state is determined according to the following calculation formula: logit(p) = 4.0877 + 0.6942 ×ANP + 0.2500 × MYL3 - 1.3552 × PPIA -0.6223 × PKIA - 0.8257 × ALDH6A1; Where logit(p) represents the probability value, ANP represents the expression level of ANP protein, MYL3 represents the expression level of MYL3 protein, ALDH6A1 represents the expression level of ALDH6A1 protein, PPIA represents the expression level of PPIA protein, and PKIA represents the expression level of PKIA protein. When p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are considered to have severe cardiac remodeling; when p < 0.5, patients with idiopathic dilated cardiomyopathy are considered to have mild cardiac remodeling.

3. The application according to claim 1 or 2, characterized in that, Compared with patients with milder cardiac remodeling in idiopathic dilated cardiomyopathy, the expression levels of ALDH6A1, PPIA, and PKIA were decreased, while the expression levels of MYL3 and ANP were increased in the sample of patients with more severe cardiac remodeling.

4. The application according to claim 1 or 2, characterized in that, Samples may include plasma, serum, or whole blood.

5. The application according to claim 1 or 2, characterized in that, The detection reagents are selected from one or more of the following methods: liquid chromatography, mass spectrometry, and immunoassay.

6. The application according to claim 5, characterized in that, Immunoassays include one or more of the following: Western blot analysis, radioimmunoassay, immunofluorescence assay, immunoprecipitation, immunodiffusion, electrochemiluminescence immunoassay, ELISA assay, and immunopolymerase chain reaction.

7. The application according to claim 5, characterized in that, The detection reagents are those applicable to liquid chromatography-tandem mass spectrometry or double antibody sandwich ELISA.

8. The application according to claim 5, characterized in that, The detection reagent contains specific antibodies or functional fragments thereof against MYL3, ANP, ALDH6A1, PPIA and PKIA.

9. The application according to claim 1 or 2, characterized in that, The products include reagent kits, chips, test strips, or systems.

10. The application according to claim 9, characterized in that, The kit is a mass spectrometry labeled peptide detection kit or an enzyme-linked immunosorbent assay kit.

11. A method for constructing a diagnostic model to assess the cardiac remodeling status in patients with idiopathic dilated cardiomyopathy, characterized in that, The construction method is based on the expression information of protein markers MYL3, ANP, ALDH6A1, PPIA, and PKIA.

12. The construction method according to claim 11, characterized in that, The construction method includes: constructing the diagnostic model using machine learning algorithms based on the expression level data of the protein biomarkers in patients with idiopathic dilated cardiomyopathy.

13. The construction method according to claim 12, characterized in that, The machine learning algorithms include linear regression, support vector machine, nearest neighbor / k-nearest neighbor, logistic regression, decision tree, k-means, random forest, naive Bayes, dimensionality reduction, and gradient boosting.

14. The construction method according to claim 13, characterized in that, The machine learning algorithm mentioned is the logistic regression algorithm.

15. The construction method according to any one of claims 11-14, characterized in that, The diagnostic model includes the following calculation formulas: logit(p) = 4.0877 + 0.6942 ×ANP + 0.2500 × MYL3 - 1.3552 × PPIA -0.6223 × PKIA - 0.8257 × ALDH6A1; Where logit(p) represents the probability value, ANP represents the expression level of ANP protein, MYL3 represents the expression level of MYL3 protein, ALDH6A1 represents the expression level of ALDH6A1 protein, PPIA represents the expression level of PPIA protein, and PKIA represents the expression level of PKIA protein.

16. The construction method according to claim 15, characterized in that, When p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are considered to have severe cardiac remodeling; when p < 0.5, patients with idiopathic dilated cardiomyopathy are considered to have mild cardiac remodeling.

17. A diagnostic system for assessing cardiac remodeling status in patients with idiopathic dilated cardiomyopathy, characterized in that, The diagnostic system includes: The data acquisition module is used to acquire the expression levels of protein markers in the plasma samples of the subjects to be tested, wherein the protein markers are ALDH6A1, PPIA, PKIA, MYL3 and ANP; The analysis module is used to analyze the expression levels of protein markers obtained by the data acquisition module to determine whether the subject's cardiac remodeling state is mild or severe; and The results output module is used to output the analysis results of the analysis module to obtain the diagnostic results of the subject's cardiac remodeling state.

18. The diagnostic system according to claim 17, characterized in that, The analysis module is used to provide the expression levels of protein markers obtained by the data acquisition module as input data to the constructed diagnostic model, and to analyze the results to determine whether the subject's cardiac remodeling state is mild or severe.

19. The diagnostic system according to claim 18, characterized in that, The diagnostic model includes the diagnostic model constructed by the construction method according to any one of claims 11-16.

20. The diagnostic system according to claim 19, characterized in that, The subjects included patients with idiopathic dilated cardiomyopathy.

21. A computer-readable storage medium, characterized in that, It stores a computer program for performing the functions of the diagnostic system according to any one of claims 17-20.

22. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the functions of the diagnostic system according to any one of claims 17-20.

Images